Effectiveness of COVID-19 Vaccination Against SARS-CoV-2 Omicron Variant Infection and Symptoms - China, December 2022-February 2023.

What is already known about this topic?: A considerable percentage of the population has received both primary and booster vaccinations, which could potentially provide protection against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron infections and related symptoms. What is added by this report?: The self-reported infection rate, as determined from an online survey, reached its peak (15.5%) between December 19 and 21, 2022, with an estimated 82.4% of individuals in China being infected as of February 7, 2023. During the epidemic, the effectiveness of booster vaccinations against SARS-CoV-2 Omicron infection was found to be 49.0% within three months of vaccination and 37.9% between 3 and 6 months following vaccination. Furthermore, the vaccine effectiveness of the booster vaccination in relation to symptom prevention varied from 48.7% to 83.2% within three months and from 25.9% to 69.0% between 3 and 6 months post-booster vaccination. What are the implications for public health practice?: The development and production of efficacious vaccines, together with prompt vaccinations or emergency vaccinations, have the potential to mitigate the epidemic's impact and safeguard public health.

Tight junction protein occludin is an internalization factor for SARS-CoV-2 infection and mediates virus cell-to-cell transmission.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spreads efficiently by spike-mediated, direct cell-to-cell transmission. However, the underlying mechanism is poorly understood. Herein, we demonstrate that the tight junction protein occludin (OCLN) is critical to this process. SARS-CoV-2 infection alters OCLN distribution and expression and causes syncytium formation that leads to viral spread. OCLN knockdown fails to alter SARS-CoV-2 binding but significantly lowers internalization, syncytium formation, and transmission. OCLN overexpression also has no effect on virus binding but enhances virus internalization, cell-to-cell transmission, and replication. OCLN directly interacts with the SARS-CoV-2 spike, and the endosomal entry pathway is involved in OCLN-mediated cell-to-cell fusion rather than in the cell surface entry pathway. All SARS-CoV-2 strains tested (prototypic, alpha, beta, gamma, delta, kappa, and omicron) are dependent on OCLN for cell-to-cell transmission, although the extent of syncytium formation differs between strains. We conclude that SARS-CoV-2 utilizes OCLN as an internalization factor for cell-to-cell transmission.

Transmission trends of the global COVID-19 pandemic with combined effects of adaptive behaviours and vaccination.

We developed a mechanism model which allows for simulating the novel coronavirus (COVID-19) transmission dynamics with the combined effects of human adaptive behaviours and vaccination, aiming at predicting the end time of COVID-19 infection in global scale. Based on the surveillance information (reported cases and vaccination data) between 22 January 2020 and 18 July 2022, we validated the model by Markov Chain Monte Carlo (MCMC) fitting method. We found that (1) if without adaptive behaviours, the epidemic could sweep the world in 2022 and 2023, causing 3.098 billion of human infections, which is 5.39 times of current number; (2) 645 million people could be avoided from infection due to vaccination; and (3) in current scenarios of protective behaviours and vaccination, infection cases would increase slowly, levelling off around 2023, and it would end completely in June 2025, causing 1.024 billion infections, with 12.5 million death. Our findings suggest that vaccination and the collective protection behaviour remain the key determinants against the global process of COVID-19 transmission.

Assessing the mechanism of citywide test-trace-isolate Zero-COVID policy and exit strategy of COVID-19 pandemic.

BACKGROUND: Countries that aimed for eliminating the cases of COVID-19 with test-trace-isolate policy are found to have lower infections, deaths, and better economic performance, compared with those that opted for other mitigation strategies. However, the continuous evolution of new strains has raised the question of whether COVID-19 eradication is still possible given the limited public health response capacity and fatigue of the epidemic. We aim to investigate the mechanism of the Zero-COVID policy on outbreak containment, and to explore the possibility of eradication of Omicron transmission using the citywide test-trace-isolate (CTTI) strategy. METHODS: We develop a compartmental model incorporating the CTTI Zero-COVID policy to understand how it contributes to the SARS-CoV-2 elimination. We employ our model to mimic the Delta outbreak in Fujian Province, China, from September 10 to October 9, 2021, and the Omicron outbreak in Jilin Province, China for the period from March 1 to April 1, 2022. Projections and sensitivity analyses were conducted using dynamical system and Latin Hypercube Sampling/ Partial Rank Correlation Coefficient (PRCC). RESULTS: Calibration results of the model estimate the Fujian Delta outbreak can end in 30 (95% confidence interval CI: 28-33) days, after 10 (95% CI: 9-11) rounds of citywide testing. The emerging Jilin Omicron outbreak may achieve zero COVID cases in 50 (95% CI: 41-57) days if supported with sufficient public health resources and population compliance, which shows the effectiveness of the CTTI Zero-COVID policy. CONCLUSIONS: The CTTI policy shows the capacity for the eradication of the Delta outbreaks and also the Omicron outbreaks. Nonetheless, the implementation of radical CTTI is challenging, which requires routine monitoring for early detection, adequate testing capacity, efficient contact tracing, and high isolation compliance, which constrain its benefits in regions with limited resources. Moreover, these challenges become even more acute in the face of more contagious variants with a high proportion of asymptomatic cases. Hence, in regions where CTTI is not possible, personal protection, public health control measures, and vaccination are indispensable for mitigating and exiting the COVID-19 pandemic.

Association Between COVID-19 Vaccination Coverage and Case Fatality Ratio: a Comparative Study - Hong Kong SAR, China and Singapore, December 2021-March 2022.

What is already known about this topic?: Hong Kong Special Administrative Region (SAR), China and Singapore are both facing considerable Omicron variant epidemic. However, the overwhelmed medical system and high case fatality ratio (CFR) just occurred in Hong Kong SAR, China but not in Singapore. What is added by this report?: The low vaccination coverage in Hong Kong SAR, China, especially among the older adults, is shown to be a primary reason of its recent high CFR. What are the implications for public health practice?: Facing the potential epidemic risk, non-vaccinated, non-fully-vaccinated, and non-booster-vaccinated people in China, especially the elderly, should get any type of accessible vaccine, which could save lives when the infection unfortunately befalls.

Association of COVID-19 Lockdown With Gestational Diabetes Mellitus.

Importance: The ongoing pandemic of COVID-19 is still affecting our life, but the effects of lockdown measures on gestational diabetes mellitus (GDM) in pregnant women remain unclear. Aim: To investigate the association between COVID-19 lockdown and GDM. Subjects and Methods: Medical records of 140844 pregnant women during 2015-2020 were extracted from 5 hospitals in Guangdong Province, China. Pregnant women who underwent the COVID-19 Level I lockdown (1/23 - 2/24/2020) during pregnancy were defined as the exposed group (N=20472) and pregnant women who underwent the same calendar months during 2015-2019 (1/23 - 2/24) were defined as the unexposed group (N=120372). Subgroup analyses were used to explore the potential susceptible exposure window of COVID-19 lockdown on GDM. Cumulative exposure is quantitatively estimated by assigning different weights to response periods with different exposure intensities. A logistic regression model was used to estimate the association between COVID-19 lockdown exposure and GDM. Results: The rates of GDM in the exposed and unexposed groups were 15.2% and 12.4%, respectively. The overall analyses showed positive associations (odds ratio, OR=1.22, 95%CI: 1.17, 1.27) between lockdown exposure and GDM risk in all pregnant women. More pronounced associations were found in women who underwent the COVID-19 lockdown in their first four months of pregnancy, and the adjusted OR values ranged from 1.24 (95%CI: 1.10, 1.39) in women with 5-8 gestational weeks (GWs) to 1.35 (95%CI: 1.20, 1.52) with < 5 GWs. In addition, we found a positive exposure-response association of cumulative lockdown exposure with the risk of GDM. Conclusions: The COVID-19 lockdown was associated with an increased risk of GDM, and the first four months of pregnancy may be the window for sensitive exposure.

[Course of disease and related epidemiological parameters of COVID-19: a prospective study based on contact tracing cohort].

Objective: To analyze the course of disease and epidemiological parameters of COVID-19 and provide evidence for making prevention and control strategies. Methods: To display the distribution of course of disease of the infectors who had close contacts with COVID-19 cases from January 1 to March 15, 2020 in Guangdong Provincial, the models of Lognormal, Weibull and gamma distribution were applied. A descriptive analysis was conducted on the basic characteristics and epidemiological parameters of course of disease. Results: In total, 515 of 11 580 close contacts were infected, with an attack rate about 4.4%, including 449 confirmed cases and 66 asymptomatic cases. Lognormal distribution was fitting best for latent period, incubation period, pre-symptomatic infection period of confirmed cases and infection period of asymptomatic cases; Gamma distribution was fitting best for infectious period and clinical symptom period of confirmed cases; Weibull distribution was fitting best for latent period of asymptomatic cases. The latent period, incubation period, pre-symptomatic infection period, infectious period and clinical symptoms period of confirmed cases were 4.50 (95%CI:3.86-5.13) days, 5.12 (95%CI:4.63-5.62) days, 0.87 (95%CI:0.67-1.07) days, 11.89 (95%CI:9.81-13.98) days and 22.00 (95%CI:21.24-22.77) days, respectively. The latent period and infectious period of asymptomatic cases were 8.88 (95%CI:6.89-10.86) days and 6.18 (95%CI:1.89-10.47) days, respectively. Conclusion: The estimated course of COVID-19 and related epidemiological parameters are similar to the existing data.

Association of COVID-19 Lockdown With Gestational Diabetes Mellitus

Importance The ongoing pandemic of COVID-19 is still affecting our life, but the effects of lockdown measures on gestational diabetes mellitus (GDM) in pregnant women remain unclear. Aim To investigate the association between COVID-19 lockdown and GDM. Subjects and Methods Medical records of 140844 pregnant women during 2015-2020 were extracted from 5 hospitals in Guangdong Province, China. Pregnant women who underwent the COVID-19 Level I lockdown (1/23 - 2/24/2020) during pregnancy were defined as the exposed group (N=20472) and pregnant women who underwent the same calendar months during 2015-2019 (1/23 - 2/24) were defined as the unexposed group (N=120372). Subgroup analyses were used to explore the potential susceptible exposure window of COVID-19 lockdown on GDM. Cumulative exposure is quantitatively estimated by assigning different weights to response periods with different exposure intensities. A logistic regression model was used to estimate the association between COVID-19 lockdown exposure and GDM. Results The rates of GDM in the exposed and unexposed groups were 15.2% and 12.4%, respectively. The overall analyses showed positive associations (odds ratio, OR=1.22, 95%CI: 1.17, 1.27) between lockdown exposure and GDM risk in all pregnant women. More pronounced associations were found in women who underwent the COVID-19 lockdown in their first four months of pregnancy, and the adjusted OR values ranged from 1.24 (95%CI: 1.10, 1.39) in women with 5-8 gestational weeks (GWs) to 1.35 (95%CI: 1.20, 1.52) with < 5 GWs. In addition, we found a positive exposure-response association of cumulative lockdown exposure with the risk of GDM. Conclusions The COVID-19 lockdown was associated with an increased risk of GDM, and the first four months of pregnancy may be the window for sensitive exposure.

When and How to Adjust Non-Pharmacological Interventions Concurrent with Booster Vaccinations Against COVID-19 - Guangdong, China, 2022.

Introduction: With the large-scale roll-out of the coronavirus disease 2019 (COVID-19) booster vaccination effort (a vaccine dose given 6 months after completing primary vaccination) in China, we explore when and how China could lift non-pharmacological interventions (NPIs) against COVID-19 in 2022. Methods: Using a modified susceptible-infectious-recovered (SIR) mathematical model, we projected the COVID-19 epidemic situation and required medical resources in Guangdong Province, China. Results: If the number of people entering from overseas recovers to 20% of the number in 2019, the epidemic in 2022 could be controlled at a low level by a containment (215 local cases) or suppression strategy (1,397 local cases). A mitigation strategy would lead to 21,722 local cases. A coexistence strategy would lead to a large epidemic with 6,850,083 local cases that would overwhelm Guangdong's medical system. With 50% or 100% recovery of the 2019 level of travelers from overseas, the epidemic could also be controlled with containment or suppression, but enormous resources, including more hotel rooms for border quarantine, will be required. However, coexistence would lead to an uncontrollable epidemic with 12,922,032 local cases. Discussion: With booster vaccinations, the number of travelers from overseas could increase slightly in 2022, but a suppression strategy would need to be maintained to ensure a controllable epidemic.

Analyzing Rail Traffic Diversion Based on Machine Learning Technique considering Transportation Security

Based on a stated preference survey, we comprehensively analyze the travel psychology of residents and the advantages and disadvantages of rail transit and conventional buses, travel time, travel cost, travel security, and vehicle comfort and investigate the relationship between the relevant influencing factors and the transition probability from rail transit to buses. A stochastic utility theory is introduced to describe the transfer behavior pertaining to travel modes, and a binary Logit model for diversion transfer is constructed. The decision tree is also used to predict the diversion transfer. Then, based on the large amount of travel willingness data obtained through the stated preference survey, a maximum likelihood estimation method is used to calibrate the parameters of the binary Logit model. The performance of the binary Logit proves to be better than that of the decision tree. Results show that the travel time most notably affects the passenger flow transfer, followed by the vehicle comfort. Finally, Guangzhou Rail Transit Line 3 is considered an example, and the diversion route planning and design are performed according to the constructed diversion transfer probability model to verify the effectiveness and practicability of the model. The research provides an effective theoretical basis and technical reference for other cities to perform rail traffic diversion planning. Based on these results, the following suggestions can be made: (1) the organization of public transportation routes, delivery volume, and travel speed outside should be improved;(2) undertaking combined operation of bus and rail transportation and integrated development is preferred;(3) the transportation management should focus on the comprehensive function development and hardware support of public transportation stations. The convenience and comfort of rail transit are closely related to the facilities and functions of the stations and their connections, which should be highly valued.

Is SARS-CoV-2 Infection a Risk Factor for Early Pregnancy Loss? ACE2 and TMPRSS2 Coexpression and Persistent Replicative Infection in Primitive Trophoblast.

BACKGROUND: SARS-CoV-2 infection in term placenta is rare. However, growing evidence suggests that susceptibility of the human placenta to infection may vary by gestational age and pathogen. For several viral infections, susceptibility appears to be greatest during early gestation. Peri-implantation placental infections that result in pre-clinical pregnancy loss would typically go undetected. Little is known about the effects of SARS-CoV-2 on the peri-implantation human placenta since this time in pregnancy can only be modeled in vitro. METHODS: We used a human embryonic stem cell (hESC)-derived model of peri-implantation placental development to assess patterns of ACE2 and TMPRSS2 transcription and protein expression in primitive trophoblast. We then infected the same trophoblast cell model with a clinical isolate of SARS-CoV-2 and documented infection dynamics. RESULTS: ACE2 and TMPRSS2 were transcribed and translated in hESC-derived trophoblast, with preferential expression in syncytialized cells. These same cells supported replicative and persistent infection by SARS-CoV-2, while non-syncytialized trophoblast cells in the same cultures did not. CONCLUSIONS: Co-expression of ACE2 and TMPRSS2 in hESC-derived trophoblast and the robust and replicative infection limited to syncytiotrophoblast equivalents support the hypothesis that increased viral susceptibility may be a defining characteristic of primitive trophoblast.

Associations of COVID-19 lockdown with gestational length and preterm birth in China.

BACKGROUND: The effects of COVID-19 lockdown measures on maternal and fetal health remain unclear. We examined the associations of COVID-19 lockdown with gestational length and preterm birth (PTB) in a Chinese population. METHODS: We obtained medical records of 595,396 singleton live infants born between 2015 and 2020 in 5 cities in Guangdong Province, South China. The exposed group (N = 101,900) included women who experienced the COVID-19 Level I lockdown (1/23-2/24/2020) during pregnancy, while the unexposed group (N = 493,496) included women who were pregnant during the same calendar months in 2015-2019. Cumulative exposure was calculated based on days exposed to different levels of emergency responses with different weighting. Generalized linear regression models were applied to estimate the associations of lockdown exposure with gestational length and risk of PTB (< 37 weeks). RESULTS: The exposed group had a shorter mean gestational length than the unexposed group (38.66 vs 38.74 weeks: adjusted ß = - 0.06 week [95%CI, - 0.07, - 0.05 week]). The exposed group also had a higher risk of PTB (5.7% vs 5.3%; adjusted OR = 1.08 [95%CI, 1.05, 1.11]). These associations seemed to be stronger when exposure occurred before or during the 23rd gestational week (GW) than during or after the 24th GW. Similarly, higher cumulative lockdown exposure was associated with a shorter gestational length and a higher risk of PTB. CONCLUSIONS: The COVID-19 lockdown measures were associated with a slightly shorter gestational length and a moderately higher risk of PTB. Early and middle pregnancy periods may be a more susceptible exposure window.

Co-benefits of nonpharmaceutical intervention against COVID-19 on infectious diseases in China: A large population-based observational study.

Background: Nonpharmaceutical interventions (NPIs) are public health measures that aim to suppress the transmission of infectious diseases, including border restrictions, quarantine and isolation, community management, social distancing, face mask usage, and personal hygiene. This research aimed to assess the co-benefits of NPIs against COVID-19 on notifiable infectious diseases (NIDs) in Guangdong Province, China. Methods: Based on NID data from the Notifiable Infectious Diseases Surveillance System in Guangdong, we first compared the incidence of NIDs during the emergency response period (weeks 4-53 of 2020) with those in the same period of 2015-2019 and then compared that with the expected incidence during the synchronous period of 2020 for each city by using a Bayesian structural time series model. Findings: A total of 514,341 cases of 39 types of NIDs were reported in Guangdong during the emergency response period in 2020, which decreased by 50·7% compared with the synchronous period during 2015-2019. It was estimated that the number of 39 NIDs during the emergency response in 2020 was 65·6% (95% credible interval [CI]: 64·0% - 68·2%) lower than expected, which means that 982,356 (95% CI: 913,443 - 1,105,170) cases were averted. The largest reduction (82·1%) was found for children aged 0-14 years. For different categories of NIDs, natural focal diseases and insect-borne infectious diseases had the greatest reduction (89·4%), followed by respiratory infectious diseases (87·4%), intestinal infectious diseases (59·4%), and blood-borne and sexually transmitted infections (18·2%). Dengue, influenza, and hand-foot-and-mouth disease were reduced by 99·3%, 95·1%, and 76·2%, respectively. Larger reductions were found in the regions with developed economies and a higher number of COVID-19 cases. Interpretation: NPIs against COVID-19 may have a large co-benefit on the prevention of other infectious diseases in Guangdong, China, and the effects have heterogeneity in populations, diseases, time and space. Funding: Key-Area Research and Development Program of Guangdong Province.

Severity-associated markers and assessment model for predicting the severity of COVID-19: a retrospective study in Hangzhou, China.

BACKGROUND: The severity of COVID-19 associates with the clinical decision making and the prognosis of COVID-19 patients, therefore, early identification of patients who are likely to develop severe or critical COVID-19 is critical in clinical practice. The aim of this study was to screen severity-associated markers and construct an assessment model for predicting the severity of COVID-19. METHODS: 172 confirmed COVID-19 patients were enrolled from two designated hospitals in Hangzhou, China. Ordinal logistic regression was used to screen severity-associated markers. Least Absolute Shrinkage and Selection Operator (LASSO) regression was performed for further feature selection. Assessment models were constructed using logistic regression, ridge regression, support vector machine and random forest. The area under the receiver operator characteristic curve (AUROC) was used to evaluate the performance of different models. Internal validation was performed by using bootstrap with 500 re-sampling in the training set, and external validation was performed in the validation set for the four models, respectively. RESULTS: Age, comorbidity, fever, and 18 laboratory markers were associated with the severity of COVID-19 (all P values < 0.05). By LASSO regression, eight markers were included for the assessment model construction. The ridge regression model had the best performance with AUROCs of 0.930 (95% CI, 0.914-0.943) and 0.827 (95% CI, 0.716-0.921) in the internal and external validations, respectively. A risk score, established based on the ridge regression model, had good discrimination in all patients with an AUROC of 0.897 (95% CI 0.845-0.940), and a well-fitted calibration curve. Using the optimal cutoff value of 71, the sensitivity and specificity were 87.1% and 78.1%, respectively. A web-based assessment system was developed based on the risk score. CONCLUSIONS: Eight clinical markers of lactate dehydrogenase, C-reactive protein, albumin, comorbidity, electrolyte disturbance, coagulation function, eosinophil and lymphocyte counts were associated with the severity of COVID-19. An assessment model constructed with these eight markers would help the clinician to evaluate the likelihood of developing severity of COVID-19 at admission and early take measures on clinical treatment.

Hospitalization Costs of COVID-19 Cases and Their Associated Factors in Guangdong, China: A Cross-Sectional Study.

Background: The ongoing COVID-19 pandemic has brought significant challenges to health system and consumed a lot of health resources. However, evidence on the hospitalization costs and their associated factors in COVID-19 cases is scarce. Objectives: To describe the total and components of hospitalization costs of COVID-19 cases, and investigate the associated factors of costs. Methods: We included 876 confirmed COVID-19 cases admitted to 33 designated hospitals from January 15th to April 27th, 2020 in Guangdong, China, and collected their demographic and clinical information. A multiple linear regression model was performed to estimate the associations of hospitalization costs with potential associated factors. Results: The median of total hospitalization costs of COVID-19 cases was $2,869.4 (IQR: $3,916.8). We found higher total costs in male (% difference: 29.7, 95% CI: 15.5, 45.6) than in female cases, in older cases than in younger ones, in severe cases (% difference: 344.8, 95% CI: 222.5, 513.6) than in mild ones, in cases with clinical aggravation than those without, in cases with clinical symptoms (% difference: 47.7, 95% CI: 26.2, 72.9) than those without, and in cases with comorbidities (% difference: 21.1%, 21.1, 95% CI: 4.4, 40.6) than those without. We also found lower non-pharmacologic therapy costs in cases treated with traditional Chinese medicine (TCM) therapy (% difference: -47.4, 95% CI: -64.5 to -22.0) than cases without. Conclusion: The hospitalization costs of COVID-19 cases in Guangdong were comparable to the national level. Factors associated with higher hospitalization costs included sex, older age, clinical severity and aggravation, clinical symptoms and comorbidities at admission. TCM therapy was found to be associated with lower costs for some non-pharmacologic therapies.

Reduction of air pollutants and associated mortality during and after the COVID-19 lockdown in China: Impacts and implications.

Although strict lockdown measurements implemented during the COVID-19 pandemic have dramatically reduced the anthropogenic-based emissions, changes in air quality and its health impacts remain unclear in China. We comprehensively described air pollution during and after the lockdown periods in 2020 compared with 2018-2019, and estimated the mortality burden indicated by the number of deaths and years of life lost (YLL) related to the air pollution changes. The mean air quality index (AQI), PM10, PM2.5, NO2, SO2 and CO concentrations during the lockdown across China declined by 18.2 (21.2%), 27.0 µg/m3 (28.9%), 10.5 µg/m3 (18.3%), 8.4 µg/m3 (44.2%), 13.1 µg/m3 (38.8%), and 0.3 mg/m3 (27.3%) respectively, when compared to the same periods during 2018-2019. We observed an increase in O3 concentration during the lockdown by 5.5 µg/m3 (10.4%), and a slight decrease after the lockdown by 3.4 µg/m3 (4.4%). As a result, there were 51.3 (95%CI: 32.2, 70.1) thousand fewer premature deaths (16.2 thousand during and 35.1 thousand after the lockdown), and 1066.8 (95%CI: 668.7, 1456.8) thousand fewer YLLs (343.3 thousand during and 723.5 thousand after the lockdown) than these in 2018-2019. Our findings suggest that the COVID-19 lockdown has caused substantial decreases in air pollutants except for O3, and that substantial human health benefits can be achieved when strict control measures for air pollution are taken to reduce emissions from vehicles and industries. Stricter tailored policy solutions of air pollution are urgently needed in China and other countries, especially in well-developed industrial regions, such as upgrading industry structure and promoting green transportation.

Impact of COVID-19 on urban mobility during post-epidemic period in megacities: from the perspectives of taxi travel and social vitality

The prevention and control of COVID-19 in megacities is under large pressure because of tens of millions and high-density populations. The majority of epidemic prevention and control policies implemented focused on travel restrictions, which severely affected urban mobility during the epidemic. Considering the impacts of epidemic and associated control policies, this study analyzes the relationship between COVID-19, travel of residents, Point of Interest (POI), and social activities from the perspective of taxi travel. First, changes in the characteristics of taxi trips at different periods were analyzed. Next, the relationship between POIs and taxi travels was established by the Geographic Information System (GIS) method, and the spatial lag model (SLM) was introduced to explore the changes in taxi travel driving force. Then, a social activities recovery level evaluation model was proposed based on the taxi travel datasets to evaluate the recovery of social activities. The results demonstrated that the number of taxi trips dropped sharply, and the travel speed, travel time, and spatial distribution of taxi trips had been significantly influenced during the epidemic period. The spatial correlation between taxi trips was gradually weakened after the outbreak of the epidemic, and the consumption travel demand of people significantly decreased while the travel demand for community life increased dramatically. The evaluation score of social activity is increased from 8.12 to 74.43 during the post-epidemic period, which may take 3-6 months to be fully recovered as a normal period. Results and models proposed in this study may provide references for the optimization of epidemic control policies and recovery of public transport in megacities during the post-epidemic period.

Experimental re-infected cats do not transmit SARS-CoV-2.

SARS-CoV-2 is the causative agent of COVID-19 and responsible for the current global pandemic. We and others have previously demonstrated that cats are susceptible to SARS-CoV-2 infection and can efficiently transmit the virus to naïve cats. Here, we address whether cats previously exposed to SARS-CoV-2 can be re-infected with SARS-CoV-2. In two independent studies, SARS-CoV-2-infected cats were re-challenged with SARS-CoV-2 at 21 days post primary challenge (DPC) and necropsies performed at 4, 7 and 14 days post-secondary challenge (DP2C). Sentinels were co-mingled with the re-challenged cats at 1 DP2C. Clinical signs were recorded, and nasal, oropharyngeal, and rectal swabs, blood, and serum were collected and tissues examined for histologic lesions. Viral RNA was transiently shed via the nasal, oropharyngeal and rectal cavities of the re-challenged cats. Viral RNA was detected in various tissues of re-challenged cats euthanized at 4 DP2C, mainly in the upper respiratory tract and lymphoid tissues, but less frequently and at lower levels in the lower respiratory tract when compared to primary SARS-CoV-2 challenged cats at 4 DPC. Viral RNA and antigen detected in the respiratory tract of the primary SARS-CoV-2 infected cats at early DPCs were absent in the re-challenged cats. Naïve sentinels co-housed with the re-challenged cats did not shed virus or seroconvert. Together, our results indicate that cats previously infected with SARS-CoV-2 can be experimentally re-infected with SARS-CoV-2; however, the levels of virus shed was insufficient for transmission to co-housed naïve sentinels. We conclude that SARS-CoV-2 infection in cats induces immune responses that provide partial, non-sterilizing immune protection against re-infection.